Method and system for verification of carbon footprint in agricultural parcels

ABSTRACT

A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending U.S. patentapplications, each of which has a common assignee and common inventors,the entireties of which are herein incorporated by reference.

SERIAL FILING NUMBER DATE TITLE (CIBO.2010) — METHOD AND SYSTEM FORCARBON FOOTPRINT DETERMINATION BASED ON REGENERATIVE PRACTICEIMPLEMENTATION (CIBO.2011) — METHOD AND SYSTEM FOR CARBON FOOTPRINTMONITORING BASED ON REGENERATIVE PRACTICE IMPLEMENTATION

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates in general to the field of regenerativeagricultural practices, and more specifically to methods and systems forcarbon footprint determination, monitoring, and verification foragricultural parcels based on implementation of regenerative managementpractices.

Description of the Related Art

Climate change is one of the most studied and discussed topics on theplanet, and this level of global concern has sparked numerousinitiatives to reduce Earth's carbon footprint. Initiatives include zerowaste recycling and reuse programs, clean energy programs, conservationmeasures, sustainable transportation programs, and carbon offset andtrading programs. This application focuses on carbon offsets from anagricultural perspective, how they are determined, and how programs togenerate those offsets are monitored and verified.

As one skilled in the art will appreciate, billions of dollars are spenteach year by countries, corporations, small businesses, and individualsto reduce greenhouse gas emissions. But more often than not, the impactof carbon footprint reduction programs is difficult to quantify.Airlines purchase carbon credits to assuage public reaction to theamount of greenhouse gas emissions associated with each flight. Consumerproduct companies invest in carbon credits in order to advertise thatthey are committed to reducing, eliminating, and reversing their carbonfootprint. Politicians mandate production of products and modes oftransportation that will reduce greenhouse gas emissions. Andindividuals make choices for which products (e.g., clothing,electricity, food) they will purchase based upon their desire to bebetter stewards of the planet. But, the problem is that theseinvestments in carbon credits tend more toward a feel-good narrativerather than a do-good narrative, primarily because it is difficult toestimate the amount of carbon footprint reduction associated with acredit, to monitor a carbon footprint reduction activity to ensure thatit is implemented and maintained in exchange for the price of thecredit, and to ultimately verify that the amount of carbon footprintreduction advertised with the carbon credit has actually been achieved.These challenges are amplified when viewed from an agriculturalperspective.

Growers (individual farmers to conglomerates) want to make a profit, butthey are also motivated to reduce greenhouse gas emissions from theirfarms, particularly if these reductions will result in increases inproductivity and sustainable production over time. Yet, becausereductions in carbon footprints are hard to estimate, they tend toimplement well known regenerative practices such as crop rotation andcover cropping, and they forego more forward looking regenerativepractices such as low-till/low-till, low nitrogen content fertilization,etc., primarily because the carbon footprint impact of most regenerativeagricultural management practices cannot be accurately determined. Inaddition, growers may be subject to acceptance of lower-pricedincentives to implement regenerative management practices simply becauseof this accuracy problem.

The process of translating regenerative management practices into farmerincentives and carbon credit valuations is currently labor intensive. Ina typical scenario, an agronomist from, say, a consumer products companyis sent out to the field of a grower who is interested in regenerativeagriculture. The agronomist queries the farmer about baseline managementpractices and then estimates how much carbon can be sequestered underone or more regenerative practices using rule-of-thumb calculations.Market conditions for the region and crop type will dictate pricing ofincentives and associated carbon credits (if the credits are brokered).And compliance verification is generally based on the honor system or isobserved in-person by representatives of the company that provided theincentives to the grower.

Therefore, what is needed are automated methods and systems that enablegrowers and carbon credit purchasers to correctly determine the carbonfootprint of a field under the grower's current (“baseline”) managementpractices and to accurately predict the reduction of greenhouse gasemissions associated with implementation of one or more regenerativepractices.

What is also needed are automated methods and systems for monitoringimplementation and maintenance of regenerative management practices on afarm or group of farms where compliance progress does not requireself-reporting or onsite evaluations.

What is further needed are automated methods and systems for verifyingcompliance with implemented regenerative practices to trust that theamount of carbon sequestered by those implemented regenerative practicesis equivalent to that offered in purchased carbon credits.

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solvingthe above-noted problems and addresses other problems, disadvantages,and limitations of the prior art by providing a superior technique foremploying a combination of public data, commercial data, field trialdata, and crop simulation data to determine, monitor, and verify thepotential and effectiveness of regenerative management practices and togenerate agro-economic metrics and objective valuations for a vastnumber of agricultural parcels. In one embodiment, a method forverifying implementation and maintenance of regenerative managementpractices in agricultural parcels is provided, the method including:determining a regenerative carbon footprint value for a parcel, wherethe determining comprises a difference of a regenerative carbonfootprint and a baseline carbon footprint, where the baseline carbonfootprint is derived by calculating greenhouse gas emissions based onsimulating crop growth under current management practices, and where theregenerative carbon footprint is derived by calculating greenhouse gasemissions based on simulating crop growth under one or more regenerativemanagement practices corresponding to a regenerative practices planproposed by a grower; approving and publishing carbon credits that arevalued according to the regenerative practices plan; for key datescorresponding to implementation and maintenance of each of the one ormore regenerative management practices, processing and evaluatingremotely sensed images against corresponding crop curves to determinecompliance/noncompliance indicators that correspond each of the keydates; and storing the compliance/noncompliance indicators within aparcel database, and determining at a verification date compliance withthe regenerative practices plan, where, if a number of compliancescorresponding to the compliance/noncompliance indicates is above athreshold at the verification date, indicating that compliance with theregenerative practices plan is verified.

One aspect of the present invention contemplates a computer-readablestorage medium storing program instructions that, when executed by acomputer, cause the computer to perform a method for verifyingmonitoring implementation and maintenance of regenerative managementpractices in agricultural parcels, the method including: determining aregenerative carbon footprint value for a parcel, where the determiningcomprises a difference of a regenerative carbon footprint and a baselinecarbon footprint, where the baseline carbon footprint is derived bycalculating greenhouse gas emissions based on simulating crop growthunder current management practices, and where the regenerative carbonfootprint is derived by calculating greenhouse gas emissions based onsimulating crop growth under one or more regenerative managementpractices corresponding to a regenerative practices plan proposed by agrower; approving and publishing carbon credits that are valuedaccording to the regenerative practices plan; for key datescorresponding to implementation and maintenance of each of the one ormore regenerative management practices, processing and evaluatingremotely sensed images against corresponding crop curves to determinecompliance/noncompliance indicators that correspond each of the keydates; and storing the compliance/noncompliance indicators within aparcel database, and determining at a verification date compliance withthe regenerative practices plan, where, if a number of compliancescorresponding to the compliance/noncompliance indicates is above athreshold at the verification data, indicating that compliance with theregenerative practices plan is verified.

Another aspect of the present invention comprehends a system forverifying implementation and maintenance of regenerative managementpractices in agricultural parcels, the system including: a CO2Esequestration server, including: a CO2E management processor, configuredto determine a regenerative carbon footprint value for a parcel, wherethe regenerative carbon footprint value comprises a difference of aregenerative carbon footprint and a baseline carbon footprint, and wherethe baseline carbon footprint is derived by calculating greenhouse gasemissions based on employing a crop simulation processor to simulatecrop growth under current management practices, and where theregenerative carbon footprint is derived by calculating greenhouse gasemissions based on employing the crop simulation processor to simulatecrop growth under one or more regenerative management practicescorresponding to a regenerative practices plan proposed by a grower, andconfigured to approve and publish carbon credits that are valuedaccording to the regenerative practices plan; and a CO2E determinationprocessor, for key dates corresponding to implementation and maintenanceof each of the one or more regenerative management practices, configuredto process and evaluate remotely sensed images provided by a remotesense processor against corresponding crop curves to determinecompliance/noncompliance indicators that correspond each of the keydates, and configured to store the compliance/noncompliance indicatorswithin a parcel database, and configured to determine at a verificationdate compliance with the regenerative practices plan, where, if a numberof compliances corresponding to the compliance/noncompliance indicatorsis above a threshold at the verification date, indicating thatcompliance with the regenerative practices plan is verified.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the presentinvention will become better understood with regard to the followingdescription and accompanying drawings where:

FIG. 1 is a block diagram illustrating an agricultural parcel carbonfootprint system according to the present invention;

FIG. 2 is a block diagram depicting an exemplary schema for a parceldatabase according to the present invention;

FIG. 3 is a block diagram featuring a system level flow for automatedcarbon footprint and regenerative potential determination associatedwith agricultural parcels within a prescribed growing region;

FIG. 4 is a flow diagram illustrating processing and ranking ofmanagement practices associated with baseline and best practices forgeneration of inputs to a crop simulation processor according to thepresent invention;

FIG. 5 is a flow diagram showing automated processing of remote senseddata for determination, monitoring, and verification of carbonsequestration within a parcel;

FIG. 6 is a flow diagram illustrating a method for automaticallydetermining regenerative carbon footprint using best practices for anagricultural parcel;

FIG. 7 is a flow diagram detailing a method for automatically monitoringa grower's implementation of regenerative management practices accordingto the present invention;

FIG. 8 is a flow diagram depicting a method for automatically verifyinga grower's implementation of regenerative management practices accordingto the present invention;

FIG. 9 is a flow diagram detailing a method for translation of carbonfootprint associated with implementation of best regenerative managementpractices into a regenerative potential metric for an agriculturalparcel relative to all other parcels within a specified growing region.

FIG. 10 is a block diagram illustrating a carbon sequestration serveraccording to the present invention;

FIG. 11 is a block diagram depicting a client device according to thepresent invention;

FIG. 12 is a diagram featuring an exemplary carbon offsets offer displayaccording to the present invention such as might be presented by theclient device of FIG. 11;

FIG. 13 is a diagram showing an exemplary detailed carbon footprintcomparison display according to the present invention such as might bepresented by the client device of FIG. 11;

FIG. 14 is a diagram illustrating an exemplary parcel regenerativepotential display according to the present invention such as might bepresented by the client device of FIG. 11;

FIG. 15 is a diagram detailing an exemplary carbon sequestrationprogress display according to the present invention such as might bepresented by the client device of FIG. 11; and

FIG. 16 is a diagram detailing an exemplary parcel search resultsdisplay according to the present invention such as might be presented bythe client device of FIG. 11.

DETAILED DESCRIPTION

Exemplary and illustrative embodiments of the invention are describedbelow. It should be understood at the outset that, although exemplaryembodiments are illustrated in the figures and described below, theprinciples of the present disclosure may be implemented using any numberof techniques, whether currently known or not. In the interest ofclarity, not all features of an actual implementation are described inthis specification, for those skilled in the art will appreciate that inthe development of any such actual embodiment, numerousimplementation-specific decisions are made to achieve specific goals,such as compliance with system-related and business-related constraints,which vary from one implementation to another. Furthermore, it will beappreciated that such a development effort might be complex andtime-consuming, but would nevertheless be a routine undertaking forthose of ordinary skill in the art having the benefit of thisdisclosure. Various modifications to the preferred embodiment will beapparent to those skilled in the art, and the general principles definedherein may be applied to other embodiments. Therefore, the presentinvention is not intended to be limited to the particular embodimentsshown and described herein but is to be accorded the widest scopeconsistent with the principles and novel features herein disclosed.

The present invention will now be described with reference to theattached figures. Various structures, systems, and devices areschematically depicted in the drawings for purposes of explanation onlyand so as to not obscure the present invention with details that arewell known to those skilled in the art. Nevertheless, the attacheddrawings are included to describe and explain illustrative examples ofthe present invention. Unless otherwise specifically noted, articlesdepicted in the drawings are not necessarily drawn to scale.

The words and phrases used herein should be understood and interpretedto have a meaning consistent with the understanding of those words andphrases by those skilled in the relevant art. No special definition of aterm or phrase (i.e., a definition that is different from the ordinaryand customary meaning as understood by those skilled in the art) isintended to be implied by consistent usage of the term or phrase herein.To the extent that a term or phrase is intended to have a specialmeaning (i.e., a meaning other than that understood by skilled artisans)such a special definition will be expressly set forth in thespecification in a definitional manner that directly and unequivocallyprovides the special definition for the term or phrase. As used in thisdisclosure, “each” refers to each member of a set, each member of asubset, each member of a group, each member of a portion, each member ofa part, etc.

Applicants note that unless the words “means for” or “step for” areexplicitly used in a particular claim, it is not intended that any ofthe appended claims or claim elements are recited in such a manner as toinvoke 35 U.S.C. § 112(f).

Definitions

Greenhouse Gases: The different gases that cause the “greenhouse effect”in Earth's atmosphere, basically causing light from the sun to betrapped as heat. The most important gases to consider for row cropagriculture are carbon dioxide (CO2) and nitrous oxide (N2O).

Greenhouse Gas Emissions: The various human activities that emit, orrelease, greenhouse gases into the air. For example, driving a car burnsfossil fuels which releases CO2 as a byproduct. In agricultural parcels,emissions occur directly from the soil as a result of soil management,from performing necessary farming activities (e.g., driving tractors,which burn fossil fuels, releasing CO2), and from manufacturing nitrogenfertilizer (which also burns fossil fuels, releasing CO2).

Carbon Sequestration: The amount of additional carbon is retained in thesoil. In some cases, the amount of carbon in the soil increases overtime and such is referred to as the amount of carbon that is beingsequestered. If the amount of carbon in the soil is decreasing over time(i.e., being released into the atmosphere as CO2), then such is referredto as a greenhouse gas emission. In any given calculation for a field,carbon is either being sequestered or emitted.

CO2e (or CO2E): A single number representing the greenhouse gas impactof the different gasses forming the greenhouse effect, where gases otherthan CO2 are converted into carbon dioxide equivalents using standardconversion techniques prescribed by the Intergovernmental Panel onClimate Change (IPCC) that are based on the effects of each of thegasses in the atmosphere.

Carbon Footprint: A single number expressed in CO2e that represents theaggregation of both greenhouse gas emissions and carbon sequestrationoccurring in a single field (or prescribed region, etc.). Since bothgreenhouse gas emissions and carbon sequestration may be converted toCO2e, a field's net total greenhouse gas emissions (i.e., greenhouse gasemissions minus carbon sequestration) is referred to as it's carbonfootprint.

Central Processing Unit (CPU): The electronic circuits (i.e.,“hardware”) that execute the instructions of a computer program (alsoknown as a “computer application,” “application,” “application program,”“app,” “computer program,” or “program”) by performing operations ondata, where the operations may include arithmetic operations, logicaloperations, or input/output operations. A CPU may also be referred to asa “processor.”

Module: As used herein, the term “module” may refer to, be part of, orinclude an application specific integrated circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and/or memory(shared, dedicated, or group) that execute one or more computerprograms, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

In view of the above background discussion on how agencies determine andassign carbon offset credits to particular regenerative practices alongwith how implementation of those practices is tracked, a discussion ofthe present invention will be provided with reference to FIGS. 1-16. Thepresent invention overcomes the problems associated with present-daycarbon offsets techniques by providing automated methods and systemsdirected toward determining the regenerative potential of anagricultural parcel in tons of carbon dioxide equivalent (CO2E) whenthat parcel is managed according to one or more best regenerativemanagement practices compared to current, baseline management practices.These automated methods and systems may also be employed to monitorprogress of the one or more best regenerative management practices overan implementation period, and my further be employed to verify thatimplementation of the one or more best regenerative management practicesresulted in sequestration of the amount of CO2E that was previouslydetermined.

Referring to FIG. 1, a block diagram is presented illustrating anagricultural parcel carbon footprint system 100 according to the presentinvention. The system 100 may include a carbon dioxide equivalent (CO2E)sequestration server 130 that is coupled to one or more client devices101-103 through the internet cloud 110. The client devices 101-103 mayinclude one or more desktop/laptop computers 101 that executedesktop/laptop client applications 104 for communication and interactionwith the CO2E sequestration server 130 through the internet cloud 110.The client devices 101-103 may also include one or more smart tabletcomputers 102 that execute tablet client applications 105 forcommunication and interaction with the CO2E sequestration server 130through the internet cloud 110. The client devices 101-103 may furtherinclude one or more smartphone devices 103 that execute smartphoneclient applications 106 for communication and interaction with the CO2Esequestration server 130 through the internet cloud 110.

The CO2E sequestration server 130 is coupled to a truth database 121, apublic database 122, a commercial database 123, and a scientificdatabase 124. Though represented in the block diagram as singledatabases 121-124, each of the databases 121-124 may comprise asubstantial number of databases through which the CO2E sequestrationserver 130 may access truth-based data, public data, commercial data,and scientific data in order to translate this data into carbonfootprints and carbon sequestration resulting from regenerativemanagement practices into agriculturally meaningful metrics andvaluations for a vast number of agricultural parcels.

Preferably, truth-based data includes data obtained directly fromgrowers and may include “as applied” data corresponding to fertilizerapplication and field trial results, namely, the measurements taken byfarming partners who plant and harvest crops under a wide range ofspecified scenarios. These field trial results are employed by the CO2Esequestration server 130 to test and improve the accuracy of cropsimulations that may be performed to generate baseline and regenerativepractices carbon footprints and to translate these footprints intoagriculturally meaningful metrics and valuations for similar parcels,where such translations are employed to scale simulations fromindividual parcels to hundreds of thousands of parcels within geographicregions.

Public data comprises a wide variety of sources such as, but not limitedto, county records, United States Department of Agriculture reports;parcel geographic coordinates data and topography; soil types andlayering (e.g., Soil Survey Geographic Database (SSURGO); historicalcrop planting, harvesting, and yield data; soil type indexes (e.g., CornStability Rating 2 (CSR2); historical and forecast weather data; andsatellite and aerial image data taken across agriculturally meaningfulspectral bands (e.g., LANDSAT, SENTINEL) that may be processed by theCO2E sequestration server 130 to understand crop types, rotations,baseline management practices (e.g., planting dates, tillage types anddates, fertilization types and dates, irrigation types and dates,harvesting dates), and stages of growth at any given time.

Commercial data may comprise any of the public data that is aggregatedor formatted for ease of access by the CO2E sequestration server 130.

Scientific data may comprise selected results of global scientificresults taken from published literature. The results are provided to theCO2E sequestration server 130 to validate crop simulations and to ensurethat the simulations are accurate across a wide range of managementscenarios and weather conditions. In one embodiment, crop simulationresults are compared to scientific research data obtained under similarmanagement practices and weather conditions.

The CO2E sequestration server 130 may include a presentation processor141 that is coupled to a parcel database 151. The presentation processor141 comprises a user interface (UX) component 142, a search enginecomponent 143, and a user database 144.

The CO2E sequestration server 130 may further comprise an agriculturalmetrics processor 152, a crop simulation processor 153, a CO2E detectionprocessor 155, and a remote sense processor 156, all of which arecoupled to the parcel database 151. The CO2E sequestration server 130may further comprise a CO2E management processor 154 that is coupled tothe CO2E detection processor 155, the remote sense processor 157, andthe crop simulation processor 153.

In operation, records corresponding to agricultural parcels in aprescribed region are created, iterated, and revised as a function ofnewly available data from one or more of the databases 121-124 andapplicable results from recent crop simulations performed by the cropsimulation processor 153. The records are stored in the parcel database151 for access by the agricultural metrics processor 152, remote senseprocessor 156, the CO2E detection processor 155, and the presentationprocessor 141. Users may execute the client applications 104-106 on theclient devices 101-103 to specify constraints, weights, and searchparameters for one or more parcel records stored within the parceldatabase 151 and to enter parameters corresponding to proposedregenerative management practices for parcels that they manage. The userinterface processor 142 executes in order to transmit display and dataentry windows to the client devices 101-103 via their respective clientapplications 104-106 to enable the users to specify the constraints,weights, and search parameters and to enter the parameters correspondingto the proposed regenerative management practices. The clientapplications 104-106 may transmit the constraints, weights, search, andregenerative practices parameters to the presentation processor 141through the internet cloud 141. In one embodiment, the constraints,weights, search, and regenerative practices parameters are stored incorresponding user records within the user database 144 to acceleratesubsequent searches. Upon receipt of the constraints, weights, search,and regenerative practices parameters, the search engine processor 143employs the corresponding user records to access one or more recordswithin the parcel database 151 that satisfy the constraints, weights,search, and regenerative practices parameters. The one or more recordswithin the parcel database 151 that satisfy the constraints, weights,search, and regenerative practices parameters may also be stored incorresponding user records within the user database 144 to acceleratesubsequent searches, and the one or more records within the parceldatabase 151 that satisfy the constraints, weights, search, andregenerative practices parameters are provided by the search engineprocessor 143 to the user interface processor 142, which formats the oneor more records for display by the client applications 104-106 on theclient devices 101-103 according to device type, and the presentationprocessor 141 transmits the one or more records to the client devices101-103 along with contextual metadata corresponding to the one or moreparcels (e.g., parcels shown on a map) that enable the users tovisualize and better comprehend results of their searches.

In one embodiment, users may iteratively refine searches by specifyingadditional constraints, weights, search, and regenerative practicesparameters to further target search results that are of interest, andthese results are additionally stored in the corresponding user recordswithin the user database 144.

Upon selection of a specific parcel record, the presentation processor141 may transmit fields within the records that are formatted by theuser interface processor 142 for display to the user along with metadatathat enable the user to visualize and comprehend the record fieldsassociated with the parcel, thus providing the user with a substantiallyimproved method for making an informed decision regarding acorresponding agricultural parcel based upon user category (e.g., smallfarmer, enterprise farming corporation, underwriter, lender, insurer,etc.).

The remote sense processor 156 may process satellite/aerial images, andmay merge selected images to determine vegetative indices, to estimatemissing image data, and to determine both baseline management practicesand the amount of CO2E that may be sequestered under one or more bestmanagement practices when compared to the baseline management practicesfor the parcel. In addition, the remote sense processor 156 inconjunction with the CO2E detection processor 155 may additionallyemploy machine learning and computer vision techniques, described infurther detail below, to infer implementation and maintenance of one ormore regenerative management practices.

The CO2E management practices processor 154 may access data from thedatabases 121-124 corresponding to baseline management practicesassociated with parcels and rank the outputs against other managementpractice data that is received from one or more of the databases121-124. In turn, the management practices processor 154 may augmentsparse or incomplete data in order to provide location-specificinferences for a number of key crop management practices including, butnot limited to planted crop type, specific cultivar or crop variety,planting data, planting density (i.e., seeds per acre and row spacing),tillage, fertilizer application (e.g., dates and amounts), andirrigation (e.g., dates and amounts). In one embodiment, highest rankedmanagement practices are employed to construct simulation inputs to thecrop simulation processor 153 for modeling of required multi-year cropsimulations. For example, management practices from the truth database121 may be ranked higher than crop simulation results. In the absence oftruth data for a parcel, state guidelines or management practices rulesof thumb may be employed to build directives for simulations. The CO2Emanagement practices processor 154 may further access data from thedatabases 121-124 to determine one or more regenerative managementpractices (e.g., crop species and maturity; planting dates; croprotation; cover cropping; tillage type; fertilizer type, amount, andtiming; and irrigation amount and timing), where the one or moreregenerative management practices are employed to construct simulationinputs to the crop simulation processor 153 for modeling of regenerativemulti-year crop simulations in order to accurately determine the amountof carbon that may be sequestered over baseline field management.

The results of the crop simulations and remotely sensed images may beemployed by the CO2E detection processor 155 to determine the carbonsequestration potential for parcels in the parcel database 151. Theresults may also be employed by the agricultural metrics processor 152to iteratively translate simulation results and data provided by thedatabases 121-124 into figures of merit (e.g., field productivity, fieldproduction stability, field regenerative potential) and an agriculturalvaluation for every parcel within the parcel database 151. In oneembodiment, the number of parcel records in the parcel database 151comprises in excess of 20 million parcels located in the United States.Though the present disclosure employs terminology and examples focusedon the United States, the present inventors note that such terminologyand examples are provided only to clearly teach aspects of the presentinvention, and that the present invention may be easily modified tocovers like embodiments in any other country or countries in the world.

The results of the crop simulations may be further be employed by theremote sense processor 156 to establish regenerative practicesattributes (e.g., crop type and maturity of a field for which a growerhas implemented one or more regenerative management practices) that mustbe exhibited on or about corresponding regenerative practicesimplementation milestones in order to automatically monitor the grower'sprogress and to verify implementation of the one or more regenerativemanagement practices at the end of a growing season, thereby enablingfinancial incentives to be paid to the grower for implementing the oneor more regenerative management practices and to accurately confirm thatcarbon credits reserved by purchasers have been realized.

In one embodiment, the crop simulation processor 153 preferablycomprises a mechanistic crop model such as the Systems Approach to LandUse Suitability (SALUS), the initial version of which was developed atMichigan State University and which has been the subject of 20 years oftesting across hundreds of fields in 46 countries, more than 25 PhDdissertations, over 200 peer-reviewed journal articles, and thousands ofacademic citations. It is not the purpose of the present application toprovide an in depth tutorial on mechanistic crop modeling, but rather todisclose how results of crop simulations performed by crop simulationprocessor 153 are employed to determine the carbon sequestrationpotential for parcels and how the results are translated intoagriculturally meaningful metrics that enables a user to make informedand meaningful decisions for one or more parcels. For a tutorial onSALUS, the reader is directed to the publications below:

-   Basso, B., & Ritchie, J. T. (2015). Simulating crop growth and    biogeochemical fluxes in response to land management using the SALUS    model. In S. K. Hamilton, J. E. Doll, & G. P. Robertson (Eds.), The    ecology of agricultural landscapes: long-term research on the path    to sustainability. New York, N.Y., USA: Oxford University Press,    252-274;-   Basso, B., Ritchie, J. T., Grace, P. R., Sartori, L. (2006).    Simulation of tillage systems impact on soil biophysical properties    using the SALUS model. Italian Journal of Agronomy, 1(4), 677. doi:    10.4081/ija.2006.677;-   Albarenque, S. M., Basso, B., Caviglia, O. P., Melchiori, R. J.    (2016). Spatio-temporal nitrogen fertilizer response in maize: field    study and modeling approach. Agronomy Journal, 108(5), 2110. doi:    10.2134/agronj2016.02.0081; and-   Partridge, T. F., Winter, J. M., Liu, L., Kendall, A. D., Basso, B.,    Hydnman, D. (2019). Mid-20th century warming hole boosts U.S. maize    yields. Environmental Research Letters. doi:    10.1088/1748-9326/ab422b

The present inventors note that the crop simulation processor 153according to the present invention is continuously improved as afunction of new scientific and truth data in order to expand todifferent crop types and to provide scaling to address practical needsof agriculture from subfield to continental scales. The aforementionedSALUS crop model is a subset of a larger simulation engine within thecrop simulation processor 153 which uses a combination of farmerreported data, government and academic statistics, and remote sensing tobuild a detailed scenario that describes genotypic conditions (i.e.,crop parameters representing genotypic potentials of a crop),environmental conditions (i.e., weather, physical soil properties, andchemical soil properties), and management conditions (e.g., plantingdates, fertilizer application dates and amounts, tillage date, depth,and material, etc.) of a growing crop. Based on these input conditions,the crop model calculates plant growth stage, plant leaf area, solarenergy absorbed through the leaves, biomass accumulated in differentplant tissues, and water and nutrient uptake by the roots, and savesoutputs for that day. These variables are calculated at every time stepuntil the crop matures.

Accordingly, the crop simulation processor 153 is configured to describedevelopment and growth of a crop within an agricultural parcel all theway from planting to maturity. Plant development describes the timing ofevents that occur during the plant life cycle that induce changes ingrowth rates and partitioning of dry matter to different plant tissues.The crop simulation processor 153 calculates state variablesrepresenting various aspects of development and growth at each dailytime step and furthermore describes the different components of the soillayers and how these interact with the environment. Thus, the cropsimulation processor 153 may estimate the amount of water and nutrientsavailable for uptake by a growing plant. Root development occurs at eachdaily time step: root tips progress through the soil layers, and rootmass increases. This results in water and nutrient uptake in soilhorizons that are in contact with the plant's rooting system,proportional to the root mass distribution in each soil horizon.Advantageously, the crop simulations performed by the crop simulationprocessor 153 reflect the complex interactions whereby soil and weatherinfluence plant growth and how plant growth subsequently changes thesoil dynamic. Outputs of the crop simulation processor 153 include, butare not limited to, yields, nitrogen stress, drought stress, biomassaccumulation, growth stages, nitrogen uptake, nitrogen use efficiency,and water use efficiency.

In one embodiment, constraints, weights, and search parameters that auser may specify to access parcel records in the parcel database 151include, but are not limited to, growing region, state, county, zipcode, Public Land Survey System (PLSS), keywords, parcel owner name,historical land use (e.g., crop type), land type (e.g., farm, dairy,ranch, forest, etc.), parcel acreage, tillable area, regenerativemanagement practices, and agricultural metrics and valuations generatedby the CO2E sequestration server 130.

As will be described in further detail below, the agricultural metricsand valuations generated by the CO2E sequestration server 130 enable auser functioning in a specific role (e.g., farmer, enterprise,underwriter, etc.) to understand the regenerative potential along withthe value of a particular parcel from the user's perspective, and totrust that implemented regenerative management practices indeedsequester the amount of carbon which was offered in carbon credits.Depending on the user's role, agricultural metrics may be expressed asproductivity of a parcel, production risk of the parcel, and theparcel's potential to be sustainably managed under one or moreregenerative management practices. For a user purely interested infarming, a parcel's productivity, stability (i.e., productivity risk),and regenerative sustainability are paramount. However, for anenterprise that is focused on reducing carbon emissions, theregenerative sustainability metric may take precedence. In oneembodiment, the agricultural valuation assigned to a parcel employs oneor more of the agricultural metrics as a function of the user's role assupplemented by comparable sales to express an agricultural value indollars as opposed to just a value that is based on comparable parcels.Advantageously, the user is exposed to a valuation of a parcels basedupon the parcel's agricultural potential, which is a substantialimprovement over that which has heretofore been provided.

The CO2E sequestration server 130 according to the present invention maycomprise one or more application programs executing thereon to performthe operations and functions described above, and which will bedisclosed in further detail with reference to FIG. 10.

Turning to FIG. 2, a block diagram is presented depicting an exemplaryschema for a parcel database 200 according to the present invention,such as the parcel database 151 of FIG. 1. The schema 200 may include ageographic feature table 201 that is linked to a plurality of featuredetail tables 204 in a one-to-many architecture. The geographic featuretable 201 may include a plurality of records 202 having a plurality ofdata fields 203. Each of the feature detail tables 204 may include aplurality of records 205 having a plurality of data fields 206. Theplurality of data fields 203 in each of the geographic feature records202 include a geographic feature ID field (GFID), which is the primarykey for the geographic feature table 201 and which is unique for each ofthe plurality of records 202. The plurality of data fields 203 in eachof the geographic feature records 202 additionally include a geographicfeature type field (GFTYPE), a boundary field (BOUNDARY), a centroidfield (CENTROID), and an area field (AREA). GFTYPE specifies one of aplurality of geographic feature types that include, but are not limitedto, parcel, county, state, and growing region. BOUNDARY includesgeographic coordinates (e.g., longitude and latitude) that describe ageographic boundary for the land area corresponding to the GFID.CENTROID includes a geographic centroid coordinates for the land areacorresponding to the GFID. And AREA includes the area (e.g., acres,square meters, etc.) of the land area corresponding to the GFID.

The plurality of data fields 206 in each of the feature detail records205 include a feature detail ID field (FDID), which is the primary keyfor a corresponding feature detail table 204 and which is unique foreach of the plurality of records 205. Each of the feature detail records205 may also include a geographic feature type ID field (GFID), which isthe foreign key that links a feature detail record 205 back to acorresponding geographic feature record 202 in the geographic featuretable 201. Each of the feature detail records 205 may further include afeature detail type field (FDTYPE) and a feature detail data field(FDDATA). FDTYPE specifies one of a plurality of feature detail typesthat include, but are not limited to, results generated by the cropsimulation processor 153 for the corresponding FDID and GFIDcombination, a particular agricultural metric (e.g., productivity score,stability score, regenerative potential score, agricultural valuation)generated by the agricultural metrics processor 152 for thecorresponding FDID and GFID combination, stability zone boundariesgenerated by the remote sense processor 156 for the corresponding FDIDand GFID combination, and descriptive metadata taken from the databases121-124 for the corresponding FDID and GFID combination.

Accordingly, a given geographic feature (e.g., a 40-acre farm in MilfordCounty, Iowa) may be described in the parcel database 151 in terms ofits geographic boundary, centroid, and area within a record 202 in thegeographic feature table 201, and this record 202 may be linked to anumber of feature detail records 205 in different feature detail tables204 that contain feature detail data for a corresponding number offeature detail types.

As one skilled in the art will appreciate, deciphering the history andpotential of a parcel is critical to understanding the parcel's ultimateregenerative potential and economic value; however, this type ofinformation is typically not available outside of hard-to-come-byoperator data, and without access to this data, those interested inassigning CO2E sequestration potential and value to a parcel aretypically limited to public soil maps and state-level productivityrankings, which are inadequate for representing actual field conditions.In addition, one skilled in the art will appreciate that most statesdon't have a consistent productivity score that allows for comparison ofparcels across states, and that state productivity scores are based onhistorical information of weather and soil. In contrast, the metrics andvaluations provided for by the present invention are 1) consistent and2) based upon topography, soil, and crop simulations that are validatedby remote sensing in test fields.

Now referring to FIG. 3, a flow diagram 300 is presented featuringsystem level flow for generation of carbon footprint potentials alongwith regenerative potential metrics associated with agricultural parcelswithin a prescribed growing region, such as might be performed by theCO2E sequestration server 130 of FIG. 1, and such as might be stored inthe exemplary parcel database records 200 of FIG. 2. Flow begins atblock 304 where databases 302 are accessed and data therefrom isautomatically cleansed of error and formatted for analysis andsimulation. In one embodiment, the databases 302 comprise databases121-124 of FIG. 1. As one skilled in the art will appreciate, one of themost challenging issues associated with the processing of so-called “bigdata” is processing lots of data from different sources that areformatted differently, updated differently, and that contain differenttypes of errors. Accordingly, the accessing, cleansing, and formattingdata in block 304 may comprise a core set of steps for each data source,namely, downloading the data, assessing and cleansing the data, andformatting and storing the data.

The downloading step may comprise automating downloads for those datasources that require more frequent updates. For example, irrigation datamay only have to be downloaded a few times per year, but weather datahas to be downloaded daily. In addition, for data that is retroactivelyupdated due to more accurate sources, the automation task may comprisedownloading more recent days in the past, comparing their data tocurrent downloads of those days, and continuing to update the data untilit stabilizes.

The downloading step may additionally comprise automating ingestion ofdifferent data formats such as, but not limited to, CSV or Excel files,public database formats, scanned images, Power Point files, and PDFfiles.

Once downloaded, the data is assessed and cleansed, which may compriseremoval of duplicate information, inferring missing values, substitutingfor unconventional characters and symbols, and removal of outlier values(e.g., tax assessment data having extra zeros). For data that will beingested regularly, the present invention contemplates automation ofassessment and cleansing. Inference of missing values may compriseemploying alternative data (e.g., using state/national averagemanagement practices in situations where county records lack such data).It is noted that assessment and cleansing of satellite/aerial data isparticularly challenging since clouds or other obstacles may obscureimages. In one embodiment, the assessing and cleansing step comprisesautomated processes to filter out images with obscured data.

The formatting and storage of the data may include determination ofwhether data may be stored in a single file or may require a database orfile store (e.g., Amazon Simple Storage Service). Preferably, smallamounts of data are stored as files (e.g., county-level USDA data) andlarge amounts of data (e.g., weather data, satellite/aerial imagery,field trials data, and parcel metadata) are stored in databases whichare accessed via a microservice architecture. One example of such is aservice that accepts parcel boundaries to access soil zones for a givenparcel. Another example is a service that accepts parcel boundaries toaccess the yearly weather for the given parcel.

At block 306, data that has been cleansed and formatted in block 304 isanalyzed for each parcel to generate inferences regarding a number ofattributes that include, but are not limited to, crop types, croprotations and cover cropping, key management practices (e.g., plantingdates; tillage types and dates; fertilization types, amounts, and dates;irrigation amounts and dates; buffer zones, drainage control, harvestingdates), and stages of crop growth at any given time. These inferencesmay be generated by the CO2E management processor 154 and the remotesense processor 156 of FIG. 1, and are provided to the data selectionblock 308. The CO2E management processor 154 is configured to make theabove inferences because it is programmed with grower managementpractices that are common to different geographical areas. Accordingly,the CO2E management processor 154 applies this information to aparticular field location in order to build a scenario of “typicalfarming” on that field. For example, given a field in central Illinois,the CO2E management processor 154 may provide the following information:“Farmers in this area typically plant a corn/soy rotation. The maturitygroup for corn is 110 RM, which would be planted around May 15. Farmerstypically apply 150 pounds of nitrogen fertilizer in a splitapplication: 100 pounds the day before planting, and 50 pounds as amid-season side-dressing. The maturity group for soy is 3.8, which wouldbe planted around May 21. No fertilizer would be applied for soy. Mostfarmers in this area use conventional tillage, but 10% of them useconservation tillage. Advantageously, the CO2E management processor 154makes these inferences to generate simulation inputs to the cropsimulation processor 153, which in turn may simulate typical results ona field even when nothing is known about the actual farmer's practiceson that exact field. In addition, the CO2E management processor 154allows simulation inputs to be built with imperfect information, namely,if some of the farmer's actual practices for the field are known, theCO2E management processor 154 is configured to provide realistic valuesfor the management practices that are unknown.

The CO2E management processor 154 bases its inferences on USDA-reportedpractices, as well as scientific knowledge of how farmers makedecisions. For example, the USDA might report that in a particularregion fertilizer amounts vary from 120 to 170 pounds, but scientificdata indicates that farmers apply more fertilizer to fields that havehigher productivity. Accordingly, the CO2E management processor 154employs this information to generate a more precise estimate of how muchfertilizer would be used on a given field. As one skilled in the artwill appreciate, the length of the growing season is different indifferent parts of the country. Plant breeders create differentvarieties of the same base plant (e.g., corn, soy, etc.) that areoptimized for different growing seasons and these varieties mature atdifferent rates. A corn cultivar may be described as “110 RM” where the“RM” stands for “relative maturity”, and the 110 means that the planttakes about 110 days from germination to maturity. In Northerncorn-growing regions such as North Dakota, the growing season is short,and farmers typically plant “short RM” varieties of corn, to ensure thecorn matures before the onset of winter. In areas where the growingseason is longer, farmers plant “longer RM” varieties of corn, whichwill take advantage of the longer growing season to produce a higheryield. Accordingly, the CO2E management processor 154 is configured toemploy this information in making inferences about baseline practices ona field where management practices data is incomplete.

The remote sense processor 156 in conjunction with the CO2E detectionprocessor 155 may make inferences associated with crop rotation, covercropping, fertilizer application, and tillage, if sufficient image datais available. Absent sufficient image data, the CO2E managementprocessor 154 may employ supplemental verification methods. For example,if there is insufficient image data available to verify a crop rotationusing remote sensing, the CO2E management processor 154 may access thedatabases 302 to read, say, electronic planting records uploaded from acombine to infer that a specified crop rotation occurred. In anotherexample, if there is insufficient image data available to verifynitrogen application (dates and amounts), the CO2E management processor154 may access the databases 302 to read electronic records (“asapplied” files produced by a combine as a real-time records of nitrogenfertilizer application that are timestamped and tagged to a specificgeographical location) to infer that a specified amount of nitrogen wasapplied on a specified date.

At the data selection block 308, the generated parcel attributes alongwith other data necessary for simulation of crops corresponding to allof the parcels are selected on a parcel by parcel basis. A subset ofdata selection may comprise building a list of inputs to a cropsimulation model within the crop simulation processor 153 of FIG. 1 thatutilize management practices for a plurality of management scenarios. Inone embodiment, the plurality of management scenarios may comprise abaseline management scenario (i.e., current management practices for aparcel) and a best regenerative practices scenario (i.e., regenerativemanagement practices that maximize carbon sequestration over a period ofgrowing seasons). In another embodiment, the plurality of managementscenarios may comprise the baseline management scenario, the bestregenerative practices scenario, and one or more better regenerativemanagement practices scenarios (i.e., selected regenerative managementpractices that increase, but not maximize, carbon sequestration over aperiod of growing seasons). Another subset of data selection maycomprise building a list of inputs to a crop simulation model within thecrop simulation processor 153 of FIG. 1 that utilize managementpractices taken from one of the plurality of management scenarios. Theprocess of building simulation inputs is shown in block 309 and may alsocomprise a set of directives that guide building of the simulationinputs according to management practices from each of the plurality ofmanagement scenarios for parcels within a given area. Preferably, thesimulation inputs are built according to automated directives that relysolely on inferred data, without human intervention. In one embodiment,data selection provides for the combination of known and inferred datathat includes management practices, soil data, and weather data(including long-term forecasts) according to the set of directives. Fromthese directives, the data selection block 308 produces a complete inputset for crop simulation. Flow then proceeds to block 310.

At block 310, the simulation inputs for all parcels within a prescribedregion are provided to the crop simulation processor 153, which executescrop simulations for respective crops corresponding to each of theparcels over a period of years under each of the plurality of managementscenarios, where the crops and number of years are provided by thesimulation inputs. In one embodiment, the prescribed region may be theentire United States. In another embodiment, the prescribed region maybe a specific growing region (e.g., the Corn Belt, the Wheat Belt). In afurther embodiment, the prescribed region may comprise a given state(e.g., Iowa). In yet another embodiment, the prescribed region maycomprise a county (e.g., Marshall County, IA). Though the prescribedregions are preferably associated within parcels within the UnitedStates, the present inventors note that the system 100 according to thepresent invention may be adapted for practice within any country in theworld. Thus, according to the inputs provided by block 308, cropsimulations are run at scale by the crop simulation processor 153 togenerate components (e.g., CO2 flux from the soil, N2O flux from thesoil, CO2 from tractor fuel use, CO2 from production of nitrogenfertilizer, etc.) from which greenhouse gas emissions in units of CO2Eare calculated, parcel yields per planting season along with a number ofother corresponding simulation outputs such as, but not limited to,plant growth stage, plant leaf area, solar energy absorbed through theleaves, biomass accumulated in different plant tissues, and water andnutrient uptake by the roots. In one embodiment, crop growth issimulated daily and outputs for each day are saved and employed asparameters for the next growing day. These variables are calculated atevery time step until a crop for each parcel matures. In one embodiment,the crop simulation processor 153 is configured to perform millions ofcrop simulations in hours and is configured to run simulations for corn,soybeans, cotton, or wheat on any agricultural location in the U.S. Thesimulation results are stored in the parcel database 318. Flow thenproceeds to block 314.

At block 314, the CO2E determination processor 155 determines CO2Esequestration for each of the parcels by calculating greenhouse gasemissions under the baseline management scenario from emissionscorresponding to one of the plurality of regenerative practicesscenarios (e.g., best practices or better practices scenarios) andconverting these emissions into units of CO2E. Accordingly, the value ofCO2E sequestration reflects the amount of CO2E that can be sequesteredin the soil by implementing one or more regenerative managementpractices over baseline management practices. The CO2E sequestrationvalues are stored in the parcel database 318 along with theircorresponding management practices scenarios. Flow then proceeds toblock 316.

At block 316, the agro-economic metrics processor 152 accesses thedatabase 318 and employs the simulation results to generate a pluralityof agro-economic metrics for every parcel in the parcel database 318,which include a regenerative potential metric for each of the parcels.Some of the plurality of metrics, as will be described in further detailbelow, may exclusively employ simulation outputs while others of themetrics may employ simulation outputs in combination with data retrievedfrom the databases 302. Some of the metrics may not require simulationoutputs, but rather may utilize satellite/aerial image data. Some of themetrics may be iteratively generated as is shown in the diagram 300,while other metrics may be directly generated. In one embodiment thefollowing agro-economic metrics are generated at block 316:

-   -   a productivity score for each parcel that is expressed as a        value from 0 to 100 relative to all other parcels within a        prescribed region;    -   a stability (or, “field reliability”) score for each parcel that        is expressed as a value from 0 to 100 relative to all other        parcels within a prescribed region; and    -   a regenerative potential score for each parcel that is expressed        as a value from 0 to 100 relative to all other parcels within a        prescribed region.

The productivity score is a measure of the ability of a particularparcel to support crop production under common management practices inits historical environment. In this context, productivity is the outcomeof interactions among the main components of a crop production systemthat impact crop performance, namely weather, soil, and managementpractices. The stability score is a measure of the risk associated withproducing crops on the particular parcel of land over a range of years.The regenerative potential score is a measure of a parcel's ability tosustainably grow crops over a range of years under best regenerativemanagement practices. The agro-economic metrics processor 152 mayfurther access the aforenoted metrics along with data from the databases302 to calculate an agricultural value for each of the parcels thatconsiders the relative scoring of the agricultural metrics assupplemented by comparable sales of surrounding parcels. In oneembodiment, as will be described in further detail below, theproductivity score for each parcel is employed in conjunction withproductivity scores for surrounding parcels along with USDA census andsales data to generate an objective valuation of each parcel that isexpressed in dollars per acre. Other embodiments contemplate employmentof other and/or additional agricultural metrics to arrive at anagricultural valuation that is meaningful to a given user's role (e.g.,farmer, enterprise farm, banking, underwriting). Further embodimentsenvision the employment of weighted agricultural metrics to generate anagricultural valuation that is meaningful to a given user's role. Theagricultural metrics along with the agricultural valuation(s) for eachof the parcels are then stored in the parcel database 318 along withother parcel data as is described above with reference to FIGS. 1-2.

Now turning to FIG. 4, a flow diagram 400 is presented illustratingprocessing and ranking of management practices associated with baselineand best practices for generation of inputs to a crop simulationprocessor according to the present invention. As alluded to above, theCO2E management processor 154 may access the outputs of the remote senseprocessor 156 to evaluate and rank the outputs against other managementpractice data that is received from one or more of the databases 121-124and from the CO2E detection processor 155. In turn, the CO2E managementprocessor 154 may augment sparse or incomplete data in order to providelocation-specific inferences for key crop management practicesincluding, but not limited to, planted crop type, specific cultivar orcrop variety, planting data, planting density (i.e., row spacing), datesand amounts of fertilizer application, and irrigation practices. Flowbegins at block 402, where it is directed to build simulation inputs forboth baseline management practices and best regenerative managementpractices in order to determine CO2E sequestration amounts for each ofthe parcels in the parcel database 151. Flow then proceeds to block 404.

At block 404, all baseline management practices datasets are selectedfor each of the parcels. As one skilled in the art will appreciate,management practice data and corresponding datasets are highly dependenton location and type of management practice. For some practices andlocations, a trusted dataset may be available, but which is incompletealong with a less-trusted, but complete dataset. In addition, availabledatasets may be more or less geographically granular ranging from stateaverages, to county averages, all the way down to data based on 30-metergrid. Finally, some management practices also may rely on a heuristicdevised by agronomists. For example, a common rule of thumb fordetermining how much fertilizer a farmer would typically use is based onthe expected amount of yield for a given crop. Flow then proceeds toblock 406.

At block 406, a next baseline management practice is selected and flowproceeds to block 408.

At block 408, all of the baseline datasets for the selected baselinemanagement practice are evaluated and ranked according to quality. Thisranking is performed by automated directives that rank the baselinedatasets according to their ability to generate inputs to the cropsimulation processor 153 to produce outputs that are accurate whencompared to field trials and scientific data. Flow then proceeds toblock 410.

At block 410, for the selected baseline management practice, the highestranked baseline management practice dataset is selected for generationof crop simulation inputs. Flow then proceeds to decision block 412.

At decision block 412, an evaluation is made to determine if there areany remaining baseline management practices whose datasets have not beenassessed, ranked, and selected for generation of crop simulation inputs.The present inventors note that the selected value for one managementpractice may affect the selection of another. For instance, if corn isselected as a crop, then the planting date must be a date which issuitable for planting corn at a given location. A different cropselection would result in a different planting date selection.Accordingly, the CO2E management processor 154 develops automateddirectives that take into account dependency ordering of differentmanagement practices. If there are remaining baseline managementpractices, then flow proceeds to block 406. If not, then flow proceedsto block 414

At block 414, best regenerative management practices datasets areselected for each of the parcels in the parcel database 151. Flow thenproceeds to block 416.

At block 416, the highest ranked baseline management practices datasetcorresponding to each management practice along with the bestregenerative management practices dataset are provided to a simulationinput builder that generates inputs for the crop simulation processor153 for both baseline management practices and best regenerativemanagement practices. Flow then proceeds to block 418.

At block 418, the method completes.

The present inventors note that though the diagram 400 illustratesselection of management practices for both baseline and bestregenerative management scenarios, the flow may be adapted to includeone or more better regenerative management scenarios, as are describedabove.

Referring now to FIG. 5 is a flow diagram 500 is presented showingautomated processing of remote sensed data for determination,monitoring, and verification of carbon sequestration within a parcel,such as may be performed by the remote sense processor 156 of FIG. 1. Inone embodiment, the remote sense processor 156 processes satelliteand/or aerial images to determine implementation and maintenance of oneor more regenerative management practices for parcels for which carboncredits and corresponding grower incentives have been reserved. As notedabove, these practices include, but are not limited to, crop type,specific cultivar or crop variety, planting data, planting density(i.e., seeds per acre and row spacing), tillage types and dates,fertilizer application (e.g., types, dates, and amounts), crop rotationand cover cropping, irrigation (e.g., dates and amounts), buffer zoning,and drainage control. The remote sense processor 156 may processsatellite and/or aerial images at frequencies commensurate withmonitoring and implementation and maintenance of the aforenoted one ormore regenerative management practices. Flow begins at block 502, whereit is directed to process satellite and/or aerial images on a date thatis selected to confirm implementation and maintenance of selected onesof the one or more regenerative management practices. Flow then proceedsto block 504

At block 504, the remote sense processor 156 accesses public and/orcommercial data to determine implementation and maintenance of selectedones of the one or more regenerative management practices. Image datamay be from the public database 122 or the commercial database 123. Asdescribed above, the images are downloaded, assessed and cleansed, andstored. Flow then proceeds to block 506.

At block 506, images that have missing data (e.g., covered by clouds)above a prescribed quality threshold are removed and images with missingdata below the prescribed quality threshold are retained. Some of themissing data in the retained images may be estimated by time-processingdata from other time-adjacent images which include that data. Flow thenproceeds to block 508.

At block 508, relevant spectral bands for a given observation arecombined to generate composite vegetative indices for subparts of theparcels according to well-known techniques. Preferably, the LandsatSurface Reflectance-derived Enhanced Vegetation Index (EVI) is employedto determine crop type and maturity. Another embodiment contemplates useof the normalized difference vegetation index (NDVI) for purposes ofdetermining crop type and maturity. In one embodiment, red, green, blue,near-infrared, and short-wave infrared spectral bands are combined togenerate composite vegetative indices. For tillage, the remote senseprocessor 156 is configured to distinguish different tillage types as afunction of the amount of residue on present on a field for, as oneskilled in the art will appreciate, different amounts of residue presenton the field result in different near-IR signatures. Accordingly, theremote sense processor 156 may employ one or more residue indices suchas, but not limited to, Normalized Difference Tillage Index (NDTI),Shortwave Infrared Normalized Difference Residue Index (SINDRI), andCellulose Absorption Index (CAI) to distinguish tillage practices in amanner substantially similar to the employment of EVI and other spectralindices to determine crop type and maturity. Flow then proceeds to block510.

At block 510, the remote sense processor 156 identifies a bestvegetative index image for each of the parcels for the prescribed dateand provides this data to the parcel database 151. Flow then proceeds toblock 512.

At block 512, the method completes.

Turning now to FIG. 6 is a flow diagram illustrating a method forautomatically determining a regenerative carbon footprint using bestpractices for an agricultural parcel. Flow begins at block 602, whereinputs for baseline management practices scenarios and best regenerativemanagement practices scenarios generated by the management practicesscenario builder flow 400 of FIG. 4 are provided by the CO2E managementprocessor 154 to the crop simulation processor 153. Flow then proceedsto block 604.

At block 604, simulations are executed by the crop simulation processor153 under the baseline management practices scenario for a specifiednumber (X) of years to generate outputs that include components that maybe employed to accurately simulate greenhouse gas emissions for eachparcel. In one embodiment, the specified number (X) of years comprises10 years. Another embodiment contemplates the specified number (X) equalto five years. Outputs for each of the X years are stored within theparcel database 151. Flow then proceeds to block 606.

At block 606, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide flux from the soil (one component of thegreenhouse gas emissions for each parcel) from the parcel database 151and calculates the carbon dioxide flux from the soil by taking thedifference in total soil organic carbon between the ends of the firstyear and last year simulations, and dividing the difference by the totalnumber of years minus one. The result is the average change in soil peryear over all years except the first. The total soil organic carbon iscomputed as the sum of three outputs of the crop simulation processor153: belowground active organic carbon (C_ActOrgBI), belowground sloworganic carbon (C_SloOrgBI), and belowground resistant organic carbon(C_ResOrgBI). The CO2E determination processor 155 then converts thetotal soil organic carbon value into total soil organic carbon dioxide.Flow then proceeds to block 608.

At block 608, the CO2E determination processor 155 retrieves outputscorresponding to nitrous oxide flux from the soil (a second component ofthe greenhouse gas emissions for each parcel) from the parcel database151 and calculates the nitrous oxide flux from the soil by multiplyingthe amount of nitrogen fertilizer applied by a standard conversionfactor, which is converted to nitrous oxide and then to carbon dioxideequivalents. In one embodiment, conversion values from the UnitedNations Intergovernmental Panel on Climate Change (IPCC) put forth inIPCC 2014, which are:

CO₂—N₂O_(SOIL)=N_(FERTILIZER)*0.0125*(44/28)*265.

This value is calculated for each simulation year and the mean is takenas an average annual CO2E component. Flow then proceeds to block 610.

At block 610, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide from tractor fuel use (a third componentof the greenhouse gas emissions for each parcel, which represents theannual carbon dioxide released to the atmosphere when tractors usefossil fuels during planting, cultivation, harvest, etc.) from theparcel database 151 and calculates this component based on formulapublished in McSwiney, C. P., Bohm, S., Grace, P. R. and Robertson, G.P. (2010), Greenhouse Gas Emissions Calculator for Grain and BiofuelFarming Systems. Journal of Natural Resources and Life SciencesEducation, 39: 125-131. doi:10.4195/jnrlse.2009.0021. Specifically, theamount of diesel per hectare used by a tractor is multiplied by theamount of carbon dioxide released when burning diesel (from the U.S.Energy Information Administration) and this is multiplied by conversionconstants. The formula is:

CO_(2-FUEL)=(47 liters diesel/ha)*(22.4 lb CO2/gal diesel)*K

-   -   where K represents conversions between kilograms and pounds and        between liters and gallons.        This value is calculated once, as it is the same for each year        of the baseline management practices scenario. Flow then        proceeds to block 612.

At block 612, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide from the production of nitrogenfertilizer (a fourth component of the greenhouse gas emissions for eachparcel, which represents carbon dioxide emissions resulting from theproduction of nitrogen fertilizer) from the parcel database 151 andcalculates this fourth component by multiplying the annual amount ofnitrogen fertilizer applied to the parcel by a conversion factorpublished in: Robertson G P, Paul E A, Harwood R R. Greenhouse gases inintensive agriculture: contributions of individual gases to theradiative forcing of the atmosphere. Science. 2000 Sep. 15; 289 (5486):1922-5. doi: 10.1126/science.289.5486.1922. PMID: 10988070. The formulaemployed is:

CO_(2-FERTILIZER-PRODUCTION)=N_(FERTILIZER)*4.51.

This value is calculated for each simulation year and the mean is takenas an average annual CO2E component. Flow then proceeds to block 614.

At block 614, the four greenhouse gas emissions components generated atblocks 606, 608, 610, and 612 are summed together to yield the annualcarbon dioxide emissions for the parcels due under the baselinemanagement practices scenario. Flow then proceeds to block 616.

At block 616, simulations are executed by the crop simulation processor153 under best regenerative management practices for a specified number(X) of years to generate outputs that include components that may beemployed to accurately simulate greenhouse gas emissions for each parcelunder the best regenerative management practices scenario. In oneembodiment, the specified number (X) of years comprises 10 years.Another embodiment contemplates the specified number (X) equal to fiveyears. Outputs for each of the X years are stored within the parceldatabase 151. Flow then proceeds to block 618.

At block 618, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide flux from the soil (the first componentof the greenhouse gas emissions for each parcel) from the parceldatabase 151 and calculates the carbon dioxide flux from the soil underthe best regenerative management practices scenario in the same manneras under the baseline management practices scenario described withreference to block 606. Flow then proceeds to block 620.

At block 620, the CO2E determination processor 155 retrieves outputscorresponding to nitrous oxide flux from the soil (the second componentof the greenhouse gas emissions for each parcel) from the parceldatabase 151 and calculates the nitrous oxide flux from the soil underthe best regenerative management practices scenario in the same manneras under the baseline management practices scenario described withreference to block 608. Flow then proceeds to block 622.

At block 622, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide from tractor fuel use (the thirdcomponent of the greenhouse gas emissions for each parcel) from theparcel database 151 and calculates the carbon dioxide from tractor fueluse under the best regenerative management practices scenario in thesame manner as under the baseline management practices scenariodescribed with reference to block 610. Flow then proceeds to block 624.

At block 624, the CO2E determination processor 155 retrieves outputscorresponding to carbon dioxide from the production of nitrogenfertilizer (the fourth component of the greenhouse gas emissions foreach parcel) from the parcel database 151 and calculates the carbondioxide from tractor fuel use under the best regenerative managementpractices scenario in the same manner as under the baseline managementpractices scenario described with reference to block 612. Flow thenproceeds to block 626.

At block 626, the four greenhouse gas emissions components generated atblocks 618, 620, 622, and 624 are summed together to yield the annualcarbon dioxide emissions for the parcels under the best regenerativemanagement practices scenario. Flow then proceeds to block 628.

At block 628 the annual carbon dioxide emissions under the baselinemanagement practices scenario determined at block 614 are subtractedfrom the annual carbon dioxide emissions under the best managementpractices scenario to yield each parcel's regenerative carbon footprintpotential value, which is stored in the parcel database 151. Flow thenproceeds to block 630.

At block 630, the method completes.

The present inventors note that though the parcel carbon footprintdetermination flow of FIG. 6 illustrates the maximum amount ofsequestration that can be achieved when implementing all bestregenerative management practices, the present invention alsocontemplates determination of a parcel's carbon footprint potentialunder selected ones of the best regenerative management practices.

Referring to FIG. 7 is a flow diagram 700 is presented detailing amethod for automatically monitoring a grower's implementation ofregenerative practices according to the present invention. Flow beginsat block 702 where it is determined to monitor implementation of one ormore best regenerative management practices committed to by a grower. Inone embodiment, the grower may have elected to implement the one or morebest regenerative management practices in exchange for an incentivepayment from a carbon credit broker. It is the purpose of the parcelCO2E sequestration monitoring flow of FIG. 7 to provide an automatedtechnique to monitor progressive implementation of the one or more bestregenerative management practices by the grower over the course of agrowing season in order to provide confirmation to purchasers ofcorresponding carbon credits that the value of greenhouse gas emissionsassociated with the carbon credits has indeed been reduced throughimplementation of the one or more best regenerative management practicesby the grower. Flow then proceeds to block 704.

At block 704, the CO2E determination processor 155 retrieves EVI datafor the parcel that corresponds to implementation of the one or morebest regenerative management practices. Flow then proceeds to block 706.

At block 706, the CO2E determination processor 155 processes theretrieved EVI data against EVI maturity curves to determine crop type,planting date, and maturity. The CO2E determination processor 155 isconfigured to infer what crop is growing in a particular field, and whenthat crop emerged from the ground. This is done by monitoring avegetative index (such as EVI or NDVI) over time. Different crops havedifferent vegetative index curves, so by observing the increases inEVI/NDVI over time, the CO2E determination processor 155 can infer whatcrop is growing and when the crop was planted. Flow then proceeds toblock 708.

At block 708, the CO2E determination processor 155 is configured toinfer tillage practices on a field, such as tillage type and tillagedate. This is done by monitoring an index (e.g., NDTI) over a period oftime. Different tillage practices lead to different amounts of residueon the field surface, so by observing the changes in NDTI over time theCO2E determination processor 155 can infer which tillage practices havebeen employed and when corresponding tillage events occurred. Forexample, as one skilled in the art will appreciate, a tillage event willcause an abrupt change in NDTI values, and this abrupt change isemployed by the CO2E determination processor 155 to detect a tillageevent and to infer timing of the event. Flow then proceeds to block 710.

At block 710, the CO2E determination processor 155 is configured toinfer irrigation practices, such as irrigation status and amount ofirrigation. To determine irrigation practices, the CO2E determinationprocessor 155 may employ a machine-learning algorithm that recognizesthe characteristic shapes of irrigated fields, which are distinctlydifferent from the characteristic shapes of non-irrigated fields. Thisis done by monitoring visual image data such as, but not limited to,Sentinel and Landsat images Fields that are irrigated have a differentappearance from fields that are not irrigated, and irrigated fieldsdiffer in appearance based upon how much water is applied (e.g.,irrigated portions of a field will be greener than unirrigated portions,and the difference between the two will be proportional to the amount ofirrigation water applied). In one embodiment, the CO2E determinationprocessor 155 employs an index based on wavelengths to detect moisturecontent of leaf surfaces. In this embodiment, the CO2E determinationprocessor 155 may employ a convolutional neural network to identifyirrigated fields using the wavelength-based index. In addition, the CO2Edetermination processor 155 is configured to detect the colors ofirrigated and non-irrigated fields and process the differences in colorto infer the amount of irrigation water that has been applied over time.In one embodiment, the CO2E determination processor 155 comprises linearmodel based on ground truth data accessed from the truth database 121,where the linear model is configured to infer irrigation types, dates,and amounts as a function of related color differences in the images.Accordingly, the appropriate wavelengths from the irrigated fields arecompared to the same wavelengths on unirrigated fields, and thedifferences in intensity of wavelengths between the irrigated andunirrigated fields are converted to differences in water applied to theirrigated fields. Preferably, the CO2E determination processor 155 mayutilize Normalized Difference Water Index (NDWI) images to infer theirrigate ion types, dates, and amounts. As one skilled in the art willappreciate, NDWI images are based on near infrared (NIR) and short-wareinfrared (SWIR) wavelengths since reflection intensity is largely afunction of the presence of chlorophyll, resulting in commensurateincreases/decreases in the absorption of light by water. High absorptionis indicated in the SWIR regions and because water does not absorb NIR,this part of the spectrum may be also employed to render NDWI indicesresistant to atmospheric effects. Results are stored in the parceldatabase 151. Flow then proceeds to block 712.

At block 712, the CO2E determination processor 155 retrieves from theparcel database 151 field history data, committed best managementpractice data, soil data, and geographical location data for the parcel.Flow then proceeds to block 714.

At block 714, the CO2E determination processor 155 processes the fieldhistory data, committed best management practice data, soil data, andgeographical location data retrieved at block 712 to determine weight ofsoil erosion over a prescribed period of time corresponding toimplementation of the regenerative management practices. As one skilledin the art will appreciate, a number of regenerative managementpractices (e.g., cover cropping, reduced tillage, etc.) result inreductions in soil erosion. In one embodiment, soil erosion isdetermined using the well-known Revised Universal Soil Loss Equation 2(RUSLE2) algorithm, an overview of which is provided by the USDA athttps://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/research/rusle2/revised-universal-soil-loss-equation-2-overview-of-rusle2/.In another embodiment, soil erosion is determined using the well-knownWind Erosion Prediction System (WEPS) that is detailed by the USDA athttps://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/tools/weps/.Another embodiment contemplates employment of both RUSLE2 and WEPS fordetermination of soil erosion weight. Once the amount of soil erosion isdetermined, the CO2E determination processor 155 converts the weight ofsoil loss to CO2E as a function of the amount of carbon present in asoil type that corresponds to the parcel. The soil loss CO2E is thenstored in the parcel database 151. Flow then proceeds to block 716.

At block 716, the CO2E determination processor 155 may retrieve “asapplied” fertilizer data from the truth database 121 and may calculatethe amount of nitrogen applied to the field. As one skilled in the artwill appreciate, “as applied” data specifies the dates, types, andamounts of fertilizer that were applied to each portion of a parcel.Flow then proceeds to block 718.

Different types of fertilizers contain different amounts of nitrogen.From a public/commercial database, one can look up a conversion factor.So, by applying the correct conversion factor and adding up the amountsapplied in different parts of the field, we can calculate the totalamount of nitrogen applied to the field, as well as the amount appliedto each part of the field.

At block 718, the amount of nitrogen applied to the field is employed tocalculate a CO2 emissions avoidance value and the CO2 emissionsavoidance value is converted to a CO2E value, which is stored in theparcel database 151. In one embodiment, calculation of the CO2 emissionsavoidance value and conversion to a CO2E value is performed according tothe IPCC guidelines for national greenhouse gas inventories. Flow thenproceeds to decision block 720.

At decision block 720, an evaluation is made to determine if theprocessed regenerative practice data processed in blocks 704-718 forassociated dates reflects ongoing compliance with implementation of theone or more best regenerative management practices. If so, then flowproceeds to block 724. If not, then flow proceeds to block 722.

At block 722, indications of appropriate practice non-compliance arestored in the parcel database along with the date of non-compliance.Knowing that from time to time a grower's implementation of regenerativepractices may vary from was originally committed to. Accordingly, in oneembodiment, the CO2E determination processor 155 may generaterecalculation control signals that direct the CO2E sequestration system100 to recalculate previously calculated carbon credits and incentivesfor the parcel based upon non-compliance and may also generate valuechange control signals to inform both the carbon credit purchaser andthe grower of respective changes in valuation. In one embodiment, thevalue change control signals may comprise messages transmitted over oneor more of the wired or wireless links 1003 to one or more of the clientdevices 101-103, where the client applications 104-106 executing thereonmay function to decrease of valuations that correspond to payments forthe carbon credits and incentive payment. Flow then proceeds to block726.

At block 724, indications of appropriate practice compliance are storedin the parcel database along with the date of compliance. Flow thenproceeds to block 716.

At block 726, the method completes.

The present inventors note that though soil loss through erosion, asdescribed with reference to block 714 is not present in the method ofFIG. 6 for determining carbon footprints under both baseline andregenerative management practices, the method may easily be modified toincorporate this additional contributor to a parcel's overall carbonfootprint.

Turning to FIG. 8, a flow diagram 800 is presented depicting a methodfor automatically verifying a grower's implementation of regenerativepractices according to the present invention. Flow begins at block 802,where a grower is solicited to implement regenerative managementpractices via displays in one of the client devices 101-103. Flow thenproceeds to block 804.

At block 804, from one of the client devices 101-103 the grower maypropose a regenerative management practices scenario to implement one ormore regenerative management practices in exchange incentive, which maybe in the form of financial payments or credits. Flow then proceeds toblock 806.

At block 806, the CO2E sequestration server 130 executes the carbonfootprint determination flow of FIG. 6 to determine that carbonsequestration potential for the parcel due resulting from implementationof the regenerative management practices scenario of block 804 over thegrower's baseline management practices scenario. Flow then proceeds toblock 808.

At block 808, the carbon sequestration potential for the parcel andproposed regenerative management practices scenario are stored in theparcel database 151, and may be converted to carbon credits as describedabove. Flow then proceeds to block 810.

At block 810, the regenerative management practices carbon credits forthe parcel are approved and published, and are accessed via one or moreof the client devices 101-103.

At block 812, the stored carbon credits for the parcel may be reservedby a purchaser via one or more of the client devices 101-103. Flow thenproceeds to block 814.

At block 814, the CO2E sequestration server 130 executes the parcel CO2Emonitoring flow of FIG. 7 at a frequency commensurate withimplementation and maintenance of the regenerative management scenarioproposed at block 804. As described with reference to FIG. 7,compliances and non-compliances along with corresponding dates arestored in the parcel database. Flow then proceeds to decision block 816.

At decision block 816, at the end of a season (or incentive period) anevaluation is made by the CO2E determination processor 155 to verifythat the grower indeed sequestered the amount of greenhouse gasemissions corresponding to reserved/purchased carbon credits throughimplementation and maintenance of the proposed regenerative managementscenario. In one embodiment, the stored compliances and non-compliancesare accessed and, for each of regenerative management practice that wasproposed, an evaluation is made to determine if the number ofcompliances for the practice is greater than a prescribed compliancenumber threshold amount, where the prescribed compliance numberthreshold amount is set to verify with 90 percent certainty that theagreed upon regenerative management practice was implemented andmaintained, thus providing verification to the purchaser. If thecompliance threshold is met, then flow proceeds to block 818. If thecompliance threshold is not met, then flow proceeds to block 820.

At block 818, the CO2E sequestration system 100 may generate firstsignals that direct the purchaser to pay for the carbon credits, and maygenerate second control signals that cause payment of the agreed uponincentive for implementing the regenerative management scenario to thegrower. In one embodiment, the first and second signals may comprisemessages transmitted over one or more of the wired or wireless links1003 to one or more of the client devices 101-103, where the clientapplications 104-106 executing thereon may function to complete transferof funds corresponding to payments for the carbon credits and incentivepayment. In one embodiment, transfer of funds may be accomplished inconjunction with third-party payment processing systems and/orthird-party carbon credit brokerage systems. Flow then proceeds to block820.

At block 820, the method completes.

Advantageously, the CO2E sequestration system 100 according to thepresent invention provides a closed loop mechanism for automaticallydetermining the amount of greenhouse gas emissions that may besequestered for a parcel under implantation of a proposed regenerativemanagement scenario, for automatically monitoring implementation andmaintenance of the regenerative management scenario by a grower, and forproviding verification to both grower and carbon credit purchaser thatthe amount of greenhouse gas emissions corresponding to purchased carboncredits has indeed been sequestered.

Now referring to FIG. 9, a flow diagram 900 is presented detailing anexemplary method for translation of determined best practices carbonfootprint into a regenerative potential metric for an agriculturalparcel relative to all other parcels within a specified growing region.As noted above, the regenerative potential metric, along with othermetrics (e.g., productivity, production stability) generated by the CO2Esequestration server 130 and available for search an display on theclient devices 101-103 enable a user to make meaningful comparisons ofparcels within an given region (e.g., township, county, state, growingregion, etc.) as a function of a user's role as opposed to just a dollarper acre value that is based on comparable parcels. Advantageously, theuser is exposed to a metric that expresses the valuation of a parcelbased upon the parcel's regenerative potential, which is a substantialimprovement over that which has heretofore been provided. The user maybe a farmer responsible for cultivation of one or more parcels, wherethe regenerative potential metric provides for a technique to assign avalue (financial or otherwise) to implementation of one or moreregenerative management practices. The user may be a carbon creditbroker responsible for buying and selling carbon credits within thecarbon offset marketplace, where the regenerative potential metricprovides for a technique to assign a market value to implementation ofone or more regenerative management practices. The user may further be apurchaser of carbon credits, where the regenerative potential metricprovides for a technique to determine if a carbon credit broker'svaluation implementation of one or more regenerative managementpractices is in fair relative to the other parcels within the specifiedgrowing region.

Flow begins at block 902, where it is determined to generateregenerative potential metrics for all parcels within a prescribedregion. Flow then proceeds to block 904.

At block 904, the CO2E sequestration server 130 executes the parcelcarbon footprint determination flow 600 of FIG. 6 for all parcels withinthe prescribed region. Flow then proceeds to block 906.

At block 906, each parcel's regenerative carbon footprint determined atblock 904 are assigned to a percentile bin relative to all otherregenerative carbon footprint values corresponding to remaining parcelsin the prescribed region. In one embodiment, the percentile bins rangefrom 0 (i.e., implying no regenerative potential) to 100 (i.e., implyingmore regenerative potential than all other parcels in the prescribedregion) in increments of one percent. Other embodiments contemplatebinning in increments of five percent and 10 percent. Flow then proceedsto block 908.

At block 908, all of the parcel's regenerative potential scores are setto be equal to the percentile bins into which they were assigned atblock 906. Accordingly, a parcel with a regenerative potential metricof, say, 90 has the potential to reduce greenhouse gas emissions thoughimplementation of best regenerative practices twice as effectively overa parcel with a regenerative potential metric of 45. Flow then proceedsto block 910.

At block 910, the method completes.

Now tuning to FIG. 10 is a block diagram illustrating a carbonsequestration server according to the present invention, such as theserver 130 of FIG. 1. The CO2E sequestration server 1000 may include oneor more central processing units (CPU) 1001 that are coupled to memory1006 having both transitory and non-transitory memory componentstherein. The CPU 1001 is also coupled to a communications circuit 1002that couples the CO2E sequestration server 1000 to the internet cloud110 via one or more wired and/or wireless links 1003. The links 1003 mayinclude, but are not limited to, Ethernet, cable, fiber optic, anddigital subscriber line (DSL). As part of the network path to andthrough the cloud 110, providers of internet connectivity (e.g., ISPs)may employ wireless technologies from point to point as well.

The CO2E sequestration server 1000 may also comprise input/outputcircuits 1004 that include, but are not limited to, data entry anddisplay devices (e.g., keyboards, monitors, touchpads, etc.). The memory1006 may be coupled to a parcel database 1005 and to the databases121-124 described with reference to FIG. 1 above. Though the CO2Esequestration server 1000 is shown directly coupled to databases 121-124and 1005, the present inventors note that interfaces to these datasources may exclusively be through the communications circuit 1002 ormay be through a combination of direct interface and through thecommunications circuit 1002, according to the source of data.

The memory 1006 may include an operating system 1007 such as, but notlimited to, Microsoft Windows, Mac OS, Unix, and Linux, where theoperating system 1007 is configured to manage execution by the CPU 1001of program instructions that are components of one or more applicationprograms. In one embodiment, a single application program comprises aplurality of code segments 1008-1016 resident in the memory 1006 andwhich are identified as a configuration code segment CONFIG 1008, aclient communications code segment CLIENT COMM 1009, a presentationprocessor code segment PRESENTATION PROC 1010, a web services codesegment WEB SERV 1011, an agricultural metrics processor code segment AGMETRICS PROC 1012, a crop simulation processor code segment CROP SIMPROC 1013, a CO2E determination processor code segment CO2E DET PROC1014, a CO2E management practices processor code segment CO2E MGMT PROC1015, and a remote sense processor code segment REM SENSE PROC 1016.

Operationally, the CO2E sequestration server 1000 may execute one ormore of the code segments 1008-1016 under control of the OS 1007 asrequired to enable the CO2E sequestration server 1000 to ingest new datafrom external data sources 121-124, to employ data from the sources121-124 in crop simulations that translate the data into values of CO2Ethat may be sequestered under one or more best management practicesalong with regenerative potential and other meaningful agriculturalmetrics and corresponding valuations for approximately 20 millionagricultural parcels, and to store these values, metrics, and valuationsin the parcel database 1005 in a manner that can be rapidly and easilysearched and accessed by users that communicate with the CO2Esequestration server 1000 over the communications circuit 1002 viaclient applications 104-106 executing on their respective client devices101-103. The CO2E sequestration server 1000 may further be configured toexecute one or more of the code segments 1008-1016 under control of theOS 1007 as required to enable the CO2E sequestration server 1000 toformat and present search results and corresponding parcel data to theclient applications 104-106 executing on their respective client devices101-103 and to receive communications therefrom that users specify tonarrow search results or to perform new searches altogether.

CONFIG 1008 may be executed to place the server 1000 into an operationalor maintenance mode, where the maintenance mode may be entered to allowfor ingestion of new data from the data sources 121-124 via automated ormanual means. CLIENT COMM 1009 may be executed to perfect reliabletransfer of information between the CO2E sequestration server 1000 andclient applications 104-106 executing on respective client devices101-103. PRESENTATION PROC 1010 may be executed to perform searches ofthe parcel database 1005, to provide search results, and to interactwith client applications 104-106 executing on respective client devices101-103 as is described above with reference to FIGS. 1-3. WEB SERV 1011may be executed to provide for formatting of information provided byPRESENTATION PROC 1010 for transmission to the client applications104-106 and for formatting of information that is provided toPRESENTATION PROC 1010 which has been received from the clientapplications 104-106.

AG METRICS PROC 1012 may be executed to perform any of the functions andoperations described above with reference to the agricultural metricsprocessor 152 of FIG. 1. CROP SIM PROC 1013 may be executed to performany of the functions and operations described above with reference tothe crop simulation processor 153 of FIG. 1. CO2E DET PROC 1014 may beexecuted to perform any of the functions and operations described abovewith reference to the CO2E determination processor 155 of FIG. 1. CO2EMGMT PROC 1015 may be executed to perform any of the functions andoperations described above with reference to the CO2E managementpractices processor 154 of FIG. 1. REM SENSE PROC 1016 may be executedto perform any of the functions and operations described above withreference to the remote sense processor 156 of FIG. 1.

Referring now to FIG. 11 is a block diagram depicting a client deviceaccording to the present invention, Now referring to FIG. 11, a blockdiagram is presented depicting a client device 1100 according to thepresent invention, such as the client devices 101-103 discussed abovewith reference to FIG. 1. The client device 1100 may include one or morecentral processing units (CPU) 1101 that are coupled to memory 1105having both transitory and non-transitory memory components therein. TheCPU 1101 is also coupled to a communications circuit 1102 that couplesthe client device 1100 to internet cloud 110 via one or more wiredand/or wireless links 1103. The links 1103 may include, but are notlimited to, Ethernet, cable, fiber optic, and digital subscriber line(DSL).

The client device 1100 may also comprise input/output circuits 1104 thatinclude, but are not limited to, data entry and display devices (e.g.,keyboards, monitors, touchpads, etc.).

The memory 1105 may include an operating system 1106 such as, but notlimited to, Microsoft Windows, Mac OS, Unix, Linux, iOS, and Android OS,where the operating system 1106 is configured to manage execution by theCPU 1101 of program instructions that are components of a clientapplication program 1107. In one embodiment, the client applicationprogram 1107 comprises a server communications code segment SERVER COMM1108 and an I/O interface code segment I/O INTERFACE 1109.

When executing on the client device 1100, the client application program1107 provides for display of information provided by the CO2Esequestration server 130, 1000 on the input/output circuits 1104 thathelp a user make decisions regarding which parameters to specify inorder to perform searches of the parcel database 151, 1005. The SERVERCOMM 1108 segment may execute to receive this information and the I/OINTERFACE segment 1109 may execute to transmit this information to theinput/output circuit 1104. Likewise, the client 1107 provides for inputof search parameters provided by the user via the input/output circuitfor transmission to the CO2E sequestration server 130, 1000 that directthe CO2E sequestration server 130, 1000 to refine an ongoing search inorder to narrow down a number of parcels that satisfy the searchparameters or to specify parameters that direct the CO2E sequestrationserver 130, 1000 to perform new searches altogether. The SERVER COMM1108 segment may execute to transmit this information and the I/OINTERFACE segment 1109 may execute to receive this information to theinput/output circuit 1104.

The functions and operations described above with reference to the CO2Esequestration server 130, 1000 according to the present invention resultin a significant improvement in this field of technology by providing asuperior technique for translating massive amounts of agricultural datafor millions of parcels into potentials of the parcels to sequestercarbon under one or more regenerative management practices and toprovide agriculturally meaningful metrics and corresponding agriculturalvaluations and that aggregate the metrics and valuation along withpublic and commercial sales data for these parcels into detailed parcelreports that are displayed on client devices 101-103, 1100. In oneembodiment, data corresponding to the detailed parcel reports are storedin the parcel database 151 for all agricultural parcel in the UnitedStates, approximately 20 million parcels. The parcel report is theculmination all of the functions and operations described above andincludes a combination of data from public datasets, commercialdatasets, management practices inference, remote sensing inference, cropsimulation results, and final metric and agricultural algorithmicresults. In one embodiment, to enable a search interface for users, theCO2E sequestration server 130, 1000 indexes key pieces of the datawithin the parcel database 151 into a full-text search engine. Thisallows users to filter through millions of parcels to retrieve thosewhich fit their particular search criteria with sub-second searchresponse time. Exemplary client device displays will now be presentedwith reference to FIGS. 12-16 that show how exemplary data is presentedto a user, how exemplary search parameters are input by the user fortransmission to the CO2E sequestration server 130, 1000, and howinformation is displayed to the user via exemplary detailed parcelreports.

Turning now to FIG. 12 is a diagram featuring an exemplary carbonoffsets offer display 1200 according to the present invention such asmight be presented by the client device 1100 of FIG. 11. The display1200 includes a freeform entry field, wherein a user may enter growingregion, state, county, zip code, Public Land Survey System (PLSS),keywords, parcel owner name, historical land use (e.g., crop type), landtype (e.g., farm, dairy, ranch, forest, etc.), parcel acreage, tillablearea, regenerative potential score, and other agricultural metrics andagricultural valuations generated by the CO2E sequestration server 130,1000. The display also features a parcel, Heritage Farm, for which 163carbon offset credits are available at a price of $20 per credit. Thecredits are available for purchase via activating a “Buy Credits”button. As noted above, the CO2E sequestration server 130, 1000 hasdetermined an amount of greenhouse gas sequestration for the HeritageFarm under the following regenerative management practices:

Cover crops to improve soil;

Reduced nitrogen fertilizer application; and

Changeover to conservation tillage practice.

Implicit in this offer are incentives that have been proffered to theparcel owner to implement and maintain the above regenerative managementpractices.

Referring to FIG. 13, a diagram is presented showing an exemplarydetailed carbon footprint comparison display 1300 according to thepresent invention such as might be presented by the client device 1100of FIG. 1. In addition to a terrain map locating a parcel (Field 43),the display 1300 shows a baseline management practices total carbonfootprint for the parcel of 1,112 pounds of CO2E along with values ofthe four component employed by the CO2E sequestration server 130, 1000to generate the footprint. The display 1300 also shows a regenerativemanagement practices total carbon footprint for the parcel of 949 poundsof CO2E along with values of the four component employed by the CO2Esequestration server 130, 1000 to generate the footprint. The display1300 further lists the regenerative management practices to which theparcel owner has been incentivized to implement. The display 1300finally shows 20-year average yields for both baseline and regenerativemanagement practices for both corn and soy.

Turning to FIG. 14, a diagram is presented illustrating an exemplaryparcel regenerative potential display 1400 according to the presentinvention such as might be presented by the client device 1100 of FIG.11. The display 1400 is in the format of a land regeneration report thatdepicts a timeline of greenhouse gas emission reductions over a 3-yearperiod if corn/soy rotation practices are implemented. The display 1400depicts each of the four components of the parcel's carbon footprintthat are output from crop simulations performed by the CO2Esequestration server 130, 1000 under baseline and regenerativepractices.

Now referring to FIG. 15, a diagram is presented detailing an exemplarycarbon sequestration progress display 1500 according to the presentinvention such as might be presented by the client device 1100 of FIG.11. As monitored through remote sensing, display 1500 indicates that theCO2E sequestration server 130, 1000 has verified on June 12 that thegrower indeed planted soy on May 1 and that a $212.34 incentive was paidon July 3. The display 1500 also indicates that the grower committed toplant cover crops on November 15 and that monitoring of this practiceimplementation is to take place on January 15. The display 1500 furthershows a progress bar for implementation of the two regenerativemanagement practices along with the amount of carbon reduction to date.

Now turning to FIG. 16, a diagram is presented detailing an exemplaryparcel search results display 1600 according to the present inventionsuch as might be presented by the client device 1100 of FIG. 11. In theright side of the display 1600, a list of parcels that meet searchparameters that the user entered in the mid portion of the display 1600.The parcels are ranked and displayed based upon how closely they meetthe criteria entered by the user in the mid portion of the display 1600.The right portion of the display 1600 is scrollable as parcels aresorted from highest rank to lowest rank. The left portion of the display1600 shows an average carbon footprint of 3,814 parcels in BremerCounty, Iowa of 344 pounds CO2E per acre per year along with a cost of$210 per acre cost to enroll. The mid portion of the display 1600comprises a freeform search field as described above, sliders thatenables a user to specify a range of regenerative potential scores,tillable area, productivity scores, and field reliability (stability)scores for search. The display 1600 further shows check boxes wherebythe user may specify different types of crops and rotations along withbaseline and regenerative management practices.

Portions of the present invention and corresponding detailed descriptionare presented in terms of software or algorithms, and symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the ones by which those ofordinary skill in the art effectively convey the substance of their workto others of ordinary skill in the art. An algorithm, as the term isused here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, a microprocessor, a central processingunit, or similar electronic computing device, that manipulates andtransforms data represented as physical, electronic quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Note also that the software implemented aspects of the invention aretypically encoded on some form of program storage medium or implementedover some type of transmission medium. The program storage medium may beelectronic (e.g., read only memory, flash read only memory, electricallyprogrammable read only memory), random access memory magnetic (e.g., afloppy disk or a hard drive) or optical (e.g., a compact disk read onlymemory, or “CD ROM”), and may be read only or random access. Similarly,the transmission medium may be metal traces, twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The invention is not limited by these aspects of any givenimplementation.

The particular embodiments disclosed above are illustrative only, andthose skilled in the art will appreciate that they can readily use thedisclosed conception and specific embodiments as a basis for designingor modifying other structures for carrying out the same purposes of thepresent invention, and that various changes, substitutions andalterations can be made herein without departing from the scope of theinvention as set forth by the appended claims. For example,components/elements of the systems and/or apparatuses may be integratedor separated. In addition, the operation of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, unless otherwise specified steps may be performed in anysuitable order.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.

What is claimed is:
 1. A method for verifying implementation andmaintenance of regenerative management practices in agriculturalparcels, the method comprising: determining a regenerative carbonfootprint value for a parcel, wherein said determining comprises adifference of a regenerative carbon footprint and a baseline carbonfootprint, wherein the baseline carbon footprint is derived bycalculating greenhouse gas emissions based on simulating crop growthunder current management practices, and wherein the regenerative carbonfootprint is derived by calculating greenhouse gas emissions based onsimulating crop growth under one or more regenerative managementpractices corresponding to a regenerative practices plan proposed by agrower; approving and publishing carbon credits that are valuedaccording to the regenerative practices plan; for key datescorresponding to implementation and maintenance of each of the one ormore regenerative management practices, processing and evaluatingremotely sensed images against corresponding crop curves to determinecompliance/noncompliance indicators that correspond each of the keydates; and storing the compliance/noncompliance indicators within aparcel database, and determining at a verification date compliance withthe regenerative practices plan, wherein, if a number of compliancescorresponding to the compliance/noncompliance indicates is above athreshold at the verification date, indicating that compliance with theregenerative practices plan is verified.
 2. The method as recited inclaim 1, further comprising: generating control signals that causepayment of an incentive to the grower for compliance with theregenerative practices plan, wherein the incentive is set based uponvaluation of the carbon credits.
 3. The method as recited in claim 1,wherein the one or more regenerative management practices compriserotation of crops to improve carbon sequestration, and wherein EnhancedVegetation Index (EVI) images and crop curves are processed andevaluated to determine compliance/noncompliance with crop types andplanting dates.
 4. The method as recited in claim 1, wherein the one ormore regenerative management practices comprise regenerative tillagepractices to improve carbon sequestration, and wherein NormalizedDifferent Tillage Index (NDTI) images and crop curves are processed andevaluated to determine compliance/noncompliance with tillage types andtillage dates.
 5. The method as recited in claim 1, further comprising:for key dates corresponding to implementation and maintenance of aregenerative irrigation practice that comprises one of said each of theone or more regenerative management practices, processing and evaluatingremotely sensed visual images against wavelength-based indexes todetermine compliance/noncompliance indicators that correspond to each ofthe key dates.
 6. The method as recited in claim 4, wherein saidprocessing and evaluating remotely sensed visual images againstwavelength-based indexes comprises: detecting colors corresponding toirrigated and non-irrigated fields within a prescribed regioncorresponding to the parcel, and inferring an amount of irrigationapplied to the parcel based upon intensities of the remotely sensedvisual images compared to the wavelength-based indices.
 7. The method asrecited in claim 1, wherein greenhouse gas emissions for both thebaseline and regenerative carbon footprints are calculated by simulatingcrop growth for 10 years to determine baseline average yearly greenhousegas emissions and regenerative average yearly greenhouse gas emissions,and wherein components of the baseline and regenerative average yearlygreenhouse gas emissions comprise average carbon dioxide flux from thesoil, nitrous oxide flux from the soil, carbon dioxide from tractor fueluse, and carbon dioxide from production of nitrogen fertilizer.
 8. Acomputer-readable storage medium storing program instructions that, whenexecuted by a computer, cause the computer to perform a method forverifying monitoring implementation and maintenance of regenerativemanagement practices in agricultural parcels, the method comprising:determining a regenerative carbon footprint value for a parcel, whereinsaid determining comprises a difference of a regenerative carbonfootprint and a baseline carbon footprint, wherein the baseline carbonfootprint is derived by calculating greenhouse gas emissions based onsimulating crop growth under current management practices, and whereinthe regenerative carbon footprint is derived by calculating greenhousegas emissions based on simulating crop growth under one or moreregenerative management practices corresponding to a regenerativepractices plan proposed by a grower; approving and publishing carboncredits that are valued according to the regenerative practices plan;for key dates corresponding to implementation and maintenance of each ofthe one or more regenerative management practices, processing andevaluating remotely sensed images against corresponding crop curves todetermine compliance/noncompliance indicators that correspond each ofthe key dates; and storing the compliance/noncompliance indicatorswithin a parcel database, and determining at a verification datecompliance with the regenerative practices plan, wherein, if a number ofcompliances corresponding to the compliance/noncompliance indicates isabove a threshold at the verification data, indicating that compliancewith the regenerative practices plan is verified.
 9. Thecomputer-readable storage medium as recited in claim 8, wherein themethod further comprises: generating control signals that cause paymentof an incentive to the grower for compliance with the regenerativepractices plan, wherein the incentive is set based upon valuation of thecarbon credits.
 10. The computer-readable storage medium as recited inclaim 8, wherein the one or more regenerative management practicescomprise rotation of crops to improve carbon sequestration, and whereinEnhanced Vegetation Index (EVI) images and crop curves are processed andevaluated to determine compliance/noncompliance with crop types andplanting dates.
 11. The computer-readable storage medium as recited inclaim 8, wherein the one or more regenerative management practicescomprise regenerative tillage practices to improve carbon sequestration,and wherein Normalized Different Tillage Index (NDTI) images and cropcurves are processed and evaluated to determine compliance/noncompliancewith tillage types and tillage dates.
 12. The computer-readable storagemedium as recited in claim 8, wherein the method further comprises: forkey dates corresponding to implementation and maintenance of aregenerative irrigation practice that comprises one of said each of theone or more regenerative management practices, processing and evaluatingremotely sensed visual images against wavelength-based indexes todetermine compliance/noncompliance indicators that correspond to each ofthe key dates.
 13. The computer-readable storage medium as recited inclaim 11, wherein said processing and evaluating remotely sensed visualimages against wavelength-based indexes comprises: detecting colorscorresponding to irrigated and non-irrigated fields within a prescribedregion corresponding to the parcel, and inferring an amount ofirrigation applied to the parcel based upon intensities of the remotelysensed visual images compared to the wavelength-based indices.
 14. Thecomputer-readable storage medium as recited in claim 8, whereingreenhouse gas emissions for both the baseline and regenerative carbonfootprints are calculated by simulating crop growth for 10 years todetermine baseline average yearly greenhouse gas emissions andregenerative average yearly greenhouse gas emissions, and whereincomponents of the baseline and regenerative average yearly greenhousegas emissions comprise average carbon dioxide flux from the soil,nitrous oxide flux from the soil, carbon dioxide from tractor fuel use,and carbon dioxide from production of nitrogen fertilizer.
 15. A systemfor verifying implementation and maintenance of regenerative managementpractices in agricultural parcels, the system comprising: a CO2Esequestration server, comprising: a CO2E management processor,configured to determine a regenerative carbon footprint value for aparcel, wherein the regenerative carbon footprint value comprises adifference of a regenerative carbon footprint and a baseline carbonfootprint, and wherein the baseline carbon footprint is derived bycalculating greenhouse gas emissions based on employing a cropsimulation processor to simulate crop growth under current managementpractices, and wherein the regenerative carbon footprint is derived bycalculating greenhouse gas emissions based on employing said cropsimulation processor to simulate crop growth under one or moreregenerative management practices corresponding to a regenerativepractices plan proposed by a grower, and configured to approve andpublish carbon credits that are valued according to said regenerativepractices plan; and a CO2E determination processor, for key datescorresponding to implementation and maintenance of each of said one ormore regenerative management practices, configured to process andevaluate remotely sensed images provided by a remote sense processoragainst corresponding crop curves to determine compliance/noncomplianceindicators that correspond each of said key dates, and configured tostore said compliance/noncompliance indicators within a parcel database,and configured to determine at a verification date compliance with saidregenerative practices plan, wherein, if a number of compliancescorresponding to said compliance/noncompliance indicators is above athreshold at said verification date, indicating that compliance withsaid regenerative practices plan is verified.
 16. The system as recitedin claim 15, wherein said CO2E sequestration server is configured togenerate control signals that cause payment of an incentive to saidgrower for compliance with said regenerative practices plan, whereinsaid incentive is set based upon valuation of said carbon credits. 17.The system as recited in claim 15, wherein said one or more regenerativemanagement practices comprise rotation of crops to improve carbonsequestration, and wherein said CO2E determination processor processesand evaluates Enhanced Vegetation Index (EVI) images and crop curves todetermine compliance/noncompliance with crop types and planting dates.17. The system as recited in claim 15, wherein said one or moreregenerative management practices comprise regenerative tillagepractices to improve carbon sequestration, and wherein said CO2Edetermination processor processes and evaluates Normalized DifferentTillage Index (NDTI) images and crop curves to determinecompliance/noncompliance with tillage types and tillage dates.
 18. Thesystem as recited in claim 15, wherein, for key dates corresponding toimplementation and maintenance of a regenerative irrigation practicethat comprises one of said each of the one or more regenerativemanagement practices, said CO2E determination processor processes andevaluates remotely sensed visual images against wavelength-based indexesto determine compliance/noncompliance indicators that correspond to eachof said key dates.
 19. The system as recited in claim 18, wherein saidCO2E determination processor processes and evaluates remotely sensedvisual images against wavelength-based indexes by detecting colorscorresponding to irrigated and non-irrigated fields within a prescribedregion corresponding to said parcel, and infers an amount of irrigationapplied to said parcel based upon intensities of said remotely sensedvisual images compared to said wavelength-based indices.
 20. The systemas recited in claim 15, wherein said greenhouse gas emissions for bothsaid baseline and said regenerative carbon footprints are calculated bysimulating crop growth for 10 years to determine baseline average yearlygreenhouse gas emissions and regenerative average yearly greenhouse gasemissions, and wherein components of said baseline and said regenerativeaverage yearly greenhouse gas emissions comprise average carbon dioxideflux from the soil, nitrous oxide flux from the soil, carbon dioxidefrom tractor fuel use, and carbon dioxide from production of nitrogenfertilizer.